For this portfolio I analyzed two songs using a variety of different metrics. These metrics helped me understand how my songs were structured, including aspects such as timbre and tempo. By applying computational methods, I was able to extract meaningful insights and identify patterns within the songs. The analysis provided a deeper understanding of how musical elements come together to form the unique sound of each track.
For this project I used an AI-tool to generate the tunes, namely JenAI, the idea of these two prompts for the tunes were specified by using ChatGPT (I made two prompts myself and let ChatGPT generate even more specific prompts)
For ‘wietske-b-1.mp3’ I used: Create a modern indie-pop track with a warm, intimate vibe, blending organic acoustic elements with subtle electronic textures. The song should feature delicate yet expressive string arrangements (such as a small string ensemble or chamber-style strings) that add depth and emotion without overpowering the core melody. The instrumentation should include gentle guitar or piano, soft percussion, and atmospheric pads or synths to enhance the dreamy, introspective feel. The track should be between 2 to 4 minutes long, suitable for a public broadcaster.
And this is what it sounds like:
For ‘wietske-b-2.mp3’ I used: Create a high-energy pop-rock track infused with modern synth elements. The song should feature driving drums, a tight bassline, and rhythmic electric guitars with a mix of clean and overdriven tones. Synths should add depth with lush pads, arpeggiated sequences, and subtle electronic effects. The track should feel anthemic and uplifting, with a dynamic build and a powerful, memorable chorus. 2-4 minutes.
And this is what it sounds like: